Data communications for a collective operation in a parallel active messaging interface of a parallel computer

ABSTRACT

Algorithm selection for data communications in a parallel active messaging interface (‘PAMI’) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and bit masks; receiving in an origin endpoint of the PAMI a collective instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint; constructing a bit mask for the received collective instruction; selecting, from among the associated algorithms and bit masks, a data communications algorithm in dependence upon the constructed bit mask; and executing the collective instruction, transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.B554331 awarded by the Department of Energy. The Government has certainrights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for algorithm selection for datacommunications for a collective operation in a parallel active messaginginterface (‘PAMI’) of a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same application (split up and specially adapted) on multipleprocessors in order to obtain results faster. Parallel computing isbased on the fact that the process of solving a problem usually can bedivided into smaller jobs, which may be carried out simultaneously withsome coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing jobs via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network lends itself to point to point operations, but a treenetwork typically is inefficient in point to point communication. A treenetwork, however, does provide high bandwidth and low latency forcertain collective operations, message passing operations where allcompute nodes participate simultaneously, such as, for example, anallgather.

There is at this time a general trend in computer processor developmentto move from multi-core to many-core processors: from dual-, tri-,quad-, hexa-, octo-core chips to ones with tens or even hundreds ofcores. In addition, multi-core chips mixed with simultaneousmultithreading, memory-on-chip, and special-purpose heterogeneous corespromise further performance and efficiency gains, especially inprocessing multimedia, recognition and networking applications. Thistrend is impacting the supercomputing world as well, where largetransistor count chips are more efficiently used by replicating cores,rather than building chips that are very fast but very inefficient interms of power utilization.

At the same time, the network link speed and number of links into andout of a compute node are dramatically increasing. IBM's BlueGene/Q™supercomputer, for example, will have a five-dimensional torus network,which implements ten bidirectional data communications links per computenode—and BlueGene/Q will support many thousands of compute nodes. Tokeep these links filled with data, DMA engines are employed, butincreasingly, the HPC community is interested in latency. Moreover, withmillions of messages in flight among compute cores and compute nodes inthe same overall computer, the problem of algorithm selection comes intothe foreground, with a need to make millions of algorithm selectionsvery, very quickly and with the possibility of selecting among dozens oreven hundreds of algorithms potentially specialized in many ways, forexample: small messages on torus network, small messages on treenetwork, medium size messages on DMA, medium messages transferring datathrough segments of shared memory in a large MPI communicator with aPAMI geometry of the same size as the communicator, large messages withrendezvous over network in a DMA communicator of medium size with a muchlarger PAMI geometry, large messages in eager algorithms, mediummessages through DMA on network in a large MPI communicator having anumber of ranks that is a power of two, medium messages through DMA onshared memory with a send buffer that is aligned on an 16-byte memoryboundary, and so on, and so on, and so on.

SUMMARY OF THE INVENTION

Methods, parallel computers, and computer program products for algorithmselection for data communications for a collective operation in aparallel active messaging interface (‘PAMI’) of a parallel computer, theparallel computer including a plurality of compute nodes that execute aparallel application, the PAMI composed of data communicationsendpoints, each endpoint specifying data communications parameters for athread of execution on a compute node, including specifications of aclient, a context, and a task, the compute nodes and the endpointscoupled for data communications through the PAMI and through datacommunications resources, including associating in the PAMI datacommunications algorithms and bit masks so that each algorithm isassociated with a separate bit mask, each bit in each mask representingthe presence or absence of a characteristic of a collective instructionto be executed by use of the algorithm associated with that mask;receiving in an origin endpoint of the PAMI a collective instruction,the collective instruction specifying transmission of a datacommunications message from the origin endpoint to at least one targetendpoint; constructing by the origin endpoint a bit mask for thereceived collective instruction, each bit in the mask representing acharacteristic of the received collective instruction; selecting by theorigin endpoint, from the associated data communications algorithms independence upon the constructed bit mask, a data communicationsalgorithm for use in executing the received collective instruction; andexecuting the received collective instruction by the origin endpoint,including transmitting, according to the selected data communicationsalgorithm from the origin endpoint to the target endpoint, the datacommunications message.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of example embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of example embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a block and network diagram of an example parallelcomputer that selects algorithms for data communications for acollective operation in a parallel active messaging interface (‘PAMI’)according to embodiments of the present invention.

FIG. 2 sets forth a block diagram of an example compute node for use inparallel computers that select algorithms for data communications for acollective operation in a PAMI according to embodiments of the presentinvention.

FIG. 3A illustrates an example Point To Point Adapter for use inparallel computers that select algorithms for data communications for acollective operation in a PAMI according to embodiments of the presentinvention.

FIG. 3B illustrates an example Collective Operations Adapter for use inparallel computers that select algorithms for data communications for acollective operation in a PAMI according to embodiments of the presentinvention.

FIG. 4 illustrates an example data communications network optimized forpoint to point operations for use in parallel computers that selectalgorithms for data communications for a collective operation in a PAMIaccording to embodiments of the present invention.

FIG. 5 illustrates an example data communications network optimized forcollective operations by organizing compute nodes in a tree for use inparallel computers that select algorithms for data communications for acollective operation in a PAMI according to embodiments of the presentinvention.

FIG. 6 sets forth a block diagram of an example protocol stack for usein parallel computers that select algorithms for data communications fora collective operation in a PAMI according to embodiments of the presentinvention.

FIG. 7 sets forth a functional block diagram of an example PAMI for usein parallel computers that select algorithms for data communications fora collective operation in a PAMI according to embodiments of the presentinvention.

FIG. 8A sets forth a functional block diagram of example datacommunications resources for use in parallel computers that selectalgorithms for data communications for a collective operation in a PAMIaccording to embodiments of the present invention.

FIG. 8B sets forth a functional block diagram of an example DMAcontroller for use in parallel computers that select algorithms for datacommunications for a collective operation in a PAMI according toembodiments of the present invention—in an architecture where the DMAcontroller is the only DMA controller on a compute node—and an originendpoint and its target endpoint are both located on the same computenode.

FIG. 9 sets forth a functional block diagram of an example PAMI for usein parallel computers that select algorithms for data communications fora collective operation in a PAMI according to embodiments of the presentinvention.

FIG. 10 sets forth a functional block diagram of example endpoints foruse in parallel computers that select algorithms for data communicationsfor a collective operation in a PAMI according to embodiments of thepresent invention.

FIG. 11 sets forth a flow chart illustrating an example method ofalgorithm selection for data communications for a collective operationin a PAMI of a parallel computer according to embodiments of the presentinvention.

FIG. 12 sets forth a diagram of an example bit mask for use in algorithmselection for data communications for a collective operation in a PAMIof a parallel computer according to embodiments of the presentinvention.

FIG. 13 sets forth a flow chart illustrating a further example method ofalgorithm selection for data communications for a collective operationin a PAMI of a parallel computer according to embodiments of the presentinvention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, computers, and computer program products for algorithmselection for data communications for a collective operation in aparallel active messaging interface (‘PAMI’) of a parallel computeraccording to embodiments of the present invention are described withreference to the accompanying drawings, beginning with FIG. 1. FIG. 1sets forth a block and network diagram of an example parallel computer(100) that selects algorithms for data communications for a collectiveoperation in a PAMI according to embodiments of the present invention.The parallel computer (100) in the example of FIG. 1 is coupled tonon-volatile memory for the computer in the form of data storage device(118), an output device for the computer in the form of printer (120),and an input/output device for the computer in the form of computerterminal (122). The parallel computer (100) in the example of FIG. 1includes a plurality of compute nodes (102).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a tree network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. Tree network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes of the parallel computer.

In addition, the compute nodes (102) of parallel computer are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperation for moving data among compute nodes of an operational group. A‘reduce’ operation is an example of a collective operation that executesarithmetic or logical functions on data distributed among the computenodes of an operational group. An operational group may be implementedas, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art applicationsmessaging module or parallel communications library, anapplication-level messaging module of computer program instructions fordata communications on parallel computers. Such an application messagingmodule is disposed in an application messaging layer in a datacommunications protocol stack. Examples of prior-art parallelcommunications libraries that may be improved for use with parallelcomputers that select algorithms for data communications for acollective operation in a PAMI of a parallel computer according toembodiments of the present invention include IBM's MPI library, the‘Parallel Virtual Machine’ (‘PVM’) library, MPICH, OpenMPI, and LAM/MPI.MPI is promulgated by the MPI Forum, an open group with representativesfrom many organizations that define and maintain the MPI standard. MPIat the time of this writing is a de facto standard for communicationamong compute nodes running a parallel program on a distributed memoryparallel computer. This specification sometimes uses MPI terminology forease of explanation, although the use of MPI as such is not arequirement or limitation of the present invention.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. In a broadcastoperation, all processes specify the same root process, whose buffercontents will be sent. Processes other than the root specify receivebuffers. After the operation, all buffers contain the message from theroot process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. All processes specify the same receive count. Thesend arguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer will be divided equally and dispersed to all processes (includingitself). Each compute node is assigned a sequential identifier termed a‘rank.’ After the operation, the root has sent sendcount data elementsto each process in increasing rank order. Rank 0 receives the firstsendcount data elements from the send buffer. Rank 1 receives the secondsendcount data elements from the send buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from computer node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical AND MPI_BAND bitwise AND MPI_LOR logical OR MPI_BOR bitwise ORMPI_LXOR logical exclusive OR MPI_BXOR bitwise exclusive OR

In addition to compute nodes, the example parallel computer (100)includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes(102) through one of the data communications networks (174). The I/Onodes (110, 114) provide I/O services between compute nodes (102) andI/O devices (118, 120, 122). I/O nodes (110, 114) are connected for datacommunications I/O devices (118, 120, 122) through local area network(‘LAN’) (130). Computer (100) also includes a service node (116) coupledto the compute nodes through one of the networks (104). Service node(116) provides service common to pluralities of compute nodes, loadingprograms into the compute nodes, starting program execution on thecompute nodes, retrieving results of program operations on the computernodes, and so on. Service node (116) runs a service application (124)and communicates with users (128) through a service applicationinterface (126) that runs on computer terminal (122).

As the term is used here, a parallel active messaging interface or‘PAMI’ (218) is a system-level messaging layer in a protocol stack of aparallel computer that is composed of data communications endpoints eachof which is specified with data communications parameters for a threadof execution on a compute node of the parallel computer. The PAMI is a‘parallel’ interface in that many instances of the PAMI operate inparallel on the compute nodes of a parallel computer. The PAMI is an‘active messaging interface’ in that data communications messages in thePAMI are active messages, ‘active’ in the sense that such messagesimplement callback functions to advise of message dispatch andinstruction completion and so on, thereby reducing the quantity ofacknowledgment traffic, and the like, burdening the data communicationresources of the PAMI.

Each data communications endpoint of a PAMI is implemented as acombination of a client, a context, and a task. A ‘client’ as the termis used in PAMI operations is a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. A ‘context’ as the term is used in PAMIoperations is composed of a subset of a client's collection of dataprocessing resources, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution. In atleast some embodiments, the context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context. A‘task’ as the term is used in PAMI operations refers to a canonicalentity, an integer or objection oriented programming object, thatrepresents in a PAMI a process of execution of the parallel application.That is, a task is typically implemented as an identifier of aparticular instance of an application executing on a compute node, acompute core on a compute node, or a thread of execution on amulti-threading compute core on a compute node. In the example of FIG.1, the compute nodes (102), as well as PAMI endpoints on the computenodes, are coupled for data communications through the PAMI (218) andthrough data communications resources such as collective network (106)and point-to-point network (108). Algorithm selection for datacommunications for collective operations by the parallel computer ofFIG. 1 is further explained with reference to an origin endpoint (352),a target endpoint (354), and a table (377) that associates bit masks(378) and data communications algorithms (380) so that each algorithm isassociated with a separate bit mask. The parallel computer of FIG. 1operates generally to select algorithms for data communications for acollective operation in a PAMI of a parallel computer according toembodiments of the present invention by receiving in an origin endpoint(352) of the PAMI a collective instruction (382), the instructionspecifying transmission of a data communications message (382) from theorigin endpoint to at least one target endpoint (354). The parallelcomputer in the example of FIG. 1 further constructs a bit masks for thereceived collective instruction, selects, from among the associatedalgorithms (380) and bit masks (378), a data communications algorithm(386) in dependence upon the constructed bit mask, and executes thecollective instruction, including transmitting, according to theselected data communications algorithm (386) from the origin endpoint(352) to the target endpoint (354), the data communications message(382).

Data communications for which algorithms are selected according toembodiments of the present invention include point-to-point sendcommunications, both eager and rendezvous, receive operations, DMA PUToperations, DMA GET operations, and so on. Some data communications,typically GETs and PUTs, are one-sided DMA operations in that there isno cooperation required from a target processor, no computation on thetarget side to complete such a PUT or GET because data is transferreddirectly to or from memory on the other side of the transfer. In thissetting, the term ‘target’ is used for either PUT or GET. A PUT targetreceives data directly into its RAM from an origin endpoint. A GETtarget provides data directly from its RAM to the origin endpoint. Thusreaders will recognize that the designation of an endpoint as an originendpoint for a transfer is a designation of the endpoint that initiatescommunications—rather than a designation of the direction of thetransfer: PUT instructions transfer data from an origin endpoint to atarget endpoint. GET instructions transfer data from a target endpointto an origin endpoint.

The origin endpoint and the target endpoint can be any two endpoints onany of the compute nodes (102), including two endpoints on the samecompute node. Collective instructions reside in a work queue of acontext and result in data transfers between two endpoints, an originendpoint and a target endpoint. Collective instructions and theirsupportive communications instructions are or can be ‘active’ in thesense that the instructions implement callback functions to administerand advise of instruction dispatch and instruction completion, therebyreducing the quantity of acknowledgment traffic required on the network.Each such data communications instruction effects a data transferbetween endpoints through some form of data communications resource, anetwork, a shared memory segment, a data communications adapter, a DMAcontroller, or the like.

The arrangement of compute nodes, networks, and I/O devices making upthe example parallel computer illustrated in FIG. 1 are for explanationonly, not for limitation of the present invention. Parallel computersthat select algorithms for data communications for a collectiveoperation in a PAMI according to embodiments of the present inventionmay include additional nodes, networks, devices, and architectures, notshown in FIG. 1, as will occur to those of skill in the art. For ease ofexplanation, the parallel computer in the example of FIG. 1 isillustrated with only one origin endpoint (352), one target endpoint(354), one association of ranges and algorithms (377), one datacommunications instruction (258), and only one data communicationsmessage (382). Readers will recognize, however, that practicalembodiments of such a parallel computer will include many originendpoints, target endpoints, associations of ranges and algorithms, datacommunications instructions, and many data communications messages. Theparallel computer (100) in the example of FIG. 1 includes sixteencompute nodes (102), whereas parallel computers that select algorithmsfor data communications for a collective operation in a PAMI accordingto some embodiments of the present invention include thousands ofcompute nodes. In addition to Ethernet and JTAG, networks in such dataprocessing systems may support many data communications protocolsincluding for example TCP (Transmission Control Protocol), IP (InternetProtocol), and others as will occur to those of skill in the art.Various embodiments of the present invention may be implemented on avariety of hardware platforms in addition to those illustrated in FIG.1.

Algorithm selection for data communications for a collective operationin a PAMI according to embodiments of the present invention is generallyimplemented on a parallel computer that includes a plurality of computenodes. In fact, such computers may include thousands of such computenodes, with a compute node typically executing at least one instance ofa parallel application. Each compute node is in turn itself a computercomposed of one or more computer processors, its own computer memory,and its own input/output (‘I/O’) adapters. For further explanation,therefore, FIG. 2 sets forth a block diagram of an example compute node(152) for use in a parallel computer that selects algorithms for datacommunications for a collective operation in a PAMI according toembodiments of the present invention. The compute node (152) of FIG. 2includes one or more computer processors (164) as well as random accessmemory (‘RAM’) (156). Each processor (164) can support multiple hardwarecompute cores (165), and each such core can in turn support multiplethreads of execution, hardware threads of execution as well as softwarethreads. Each processor (164) is connected to RAM (156) through ahigh-speed front side bus (161), bus adapter (194), and a high-speedmemory bus (154)—and through bus adapter (194) and an extension bus(168) to other components of the compute node. Stored in RAM (156) is anapplication program (158), a module of computer program instructionsthat carries out parallel, user-level data processing using parallelalgorithms.

Also stored RAM (156) is an application messaging module (216), alibrary of computer program instructions that carry outapplication-level parallel communications among compute nodes, includingpoint to point operations as well as collective operations. Although theapplication program can call PAMI routines directly, the applicationprogram (158) often executes point-to-point data communicationsoperations by calling software routines in the application messagingmodule (216), which in turn is improved according to embodiments of thepresent invention to use PAMI functions to implement suchcommunications. An application messaging module can be developed fromscratch to use a PAMI according to embodiments of the present invention,using a traditional programming language such as the C programminglanguage or C++, for example, and using traditional programming methodsto write parallel communications routines that send and receive dataamong PAMI endpoints and compute nodes through data communicationsnetworks or shared-memory transfers. In this approach, the applicationmessaging module (216) exposes a traditional interface, such as MPI, tothe application program (158) so that the application program can gainthe benefits of a PAMI with no need to recode the application. As analternative to coding from scratch, therefore, existing prior artapplication messaging modules may be improved to use the PAMI, existingmodules that already implement a traditional interface. Examples ofprior-art application messaging modules that can be improved to selectalgorithms for data communications for a collective operation in a PAMIaccording to embodiments of the present invention include such parallelcommunications libraries as the traditional ‘Message Passing Interface’(‘MPI’) library, the ‘Parallel Virtual Machine’ (‘PVM’) library, MPICH,and the like.

Also represented in RAM in the example of FIG. 2 is a PAMI (218).Readers will recognize, however, that the representation of the PAMI inRAM is a convention for ease of explanation rather than a limitation ofthe present invention, because the PAMI and its components, endpoints,clients, contexts, and so on, have particular associations with andinclusions of hardware data communications resources. In fact, the PAMIcan be implemented partly as software or firmware and hardware—or even,at least in some embodiments, entirely in hardware.

Also represented in RAM (156) in the example of FIG. 2 is a segment(227) of shared memory. In typical operation, the operating system (162)in this example compute node assigns portions of address space to eachprocessor (164), and, to the extent that the processors include multiplecompute cores (165), treats each compute core as a separate processorwith its own assignment of a portion of core memory or RAM (156) for aseparate heap, stack, memory variable storage, and so on. The defaultarchitecture for such apportionment of memory space is that eachprocessor or compute core operates its assigned portion of memoryseparately, with no ability to access memory assigned to anotherprocessor or compute core. Upon request, however, the operating systemgrants to one processor or compute core the ability to access a segmentof memory that is assigned to another processor or compute core, andsuch a segment is referred to in this specification as a ‘segment ofshared memory.’

In the example of FIG. 2, each processor or compute core has uniformaccess to the RAM (156) on the compute node, so that accessing a segmentof shared memory is equally fast regardless where the shared segment islocated in physical memory. In some embodiments, however, modules ofphysical memory are dedicated to particular processors, so that aprocessor may access local memory quickly and remote memory more slowly,a configuration referred to as a Non-Uniform Memory Access or ‘NUMA.’ Insuch embodiments, a segment of shared memory can be configured locallyfor one endpoint and remotely for another endpoint—or remotely from bothendpoints of a communication. From the perspective of an origin endpointtransmitting data through a segment of shared memory that is configuredremotely with respect to the origin endpoint, transmitting data throughthe segment of shared memory will appear slower that if the segment ofshared memory were configured locally with respect to the originendpoint—or if the segment were local to both the origin endpoint andthe target endpoint. This is the effect of the architecture representedby the compute node (152) in the example of FIG. 2 with all processorsand all compute cores coupled through the same bus to the RAM—that allaccesses to segments of memory shared among processes or processors onthe compute node are local—and therefore very fast.

Also stored in RAM (156) in the example compute node of FIG. 2 is anoperating system (162), a module of computer program instructions androutines for an application program's access to other resources of thecompute node. It is possible, in some embodiments at least, for anapplication program, an application messaging module, and a PAMI in acompute node of a parallel computer to run threads of execution with nouser login and no security issues because each such thread is entitledto complete access to all resources of the node. The quantity andcomplexity of duties to be performed by an operating system on a computenode in a parallel computer therefore can be somewhat smaller and lesscomplex than those of an operating system on a serial computer with manythreads running simultaneously with various level of authorization foraccess to resources. In addition, there is no video I/O on the computenode (152) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down or ‘lightweight’ version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may be improved or simplifiedfor use in a compute node according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art.

The example compute node (152) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters for use in computers that select algorithms fordata communications for a collective operation in a parallel activemessaging interface (‘PAMI’) according to embodiments of the presentinvention include modems for wired communications, Ethernet (IEEE 802.3)adapters for wired network communications, and 802.11b adapters forwireless network communications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also used as amechanism for debugging embedded systems, providing a convenient “backdoor” into the system. The example compute node of FIG. 2 may be allthree of these: It typically includes one or more integrated circuitsinstalled on a printed circuit board and may be implemented as anembedded system having its own processor, its own memory, and its ownI/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processor registers and memory in compute node(152) for use in data communications for a collective operation in aPAMI according to embodiments of the present invention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a data communications network (108) that isoptimal for point to point message passing operations such as, forexample, a network configured as a three-dimensional torus or mesh.Point To Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). For ease of explanation, the Point To Point Adapter (180)of FIG. 2 as illustrated is configured for data communications in threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in data communicationsfor a collective operation in a PAMI of a parallel computer according toembodiments of the present invention may in fact be implemented so as tosupport communications in two dimensions, four dimensions, fivedimensions, and so on.

The data communications adapters in the example of FIG. 2 includes aCollective Operations Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations such as, for example, a networkconfigured as a binary tree. Collective Operations Adapter (188)provides data communications through three bidirectional links: two tochildren nodes (190) and one to a parent node (192).

The example compute node (152) includes a number of arithmetic logicunits (‘ALUs’). ALUs (166) are components of processors (164), and aseparate ALU (170) is dedicated to the exclusive use of collectiveoperations adapter (188) for use in performing the arithmetic andlogical functions of reduction operations. Computer program instructionsof a reduction routine in an application messaging module (216) or aPAMI (218) may latch an instruction for an arithmetic or logicalfunction into instruction register (169). When the arithmetic or logicalfunction of a reduction operation is a ‘sum’ or a ‘logical OR,’ forexample, collective operations adapter (188) may execute the arithmeticor logical operation by use of an ALU (166) in a processor (164) or,typically much faster, by use of the dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(‘DMA’) controller (225), a module of automated computing machinery thatimplements, through communications with other DMA engines on othercompute nodes, or on a same compute node, direct memory access to andfrom memory on its own compute node as well as memory on other computenodes. Direct memory access is a way of reading and writing to and frommemory of compute nodes with reduced operational burden on computerprocessors (164); a CPU initiates a DMA transfer, but the CPU does notexecute the DMA transfer. A DMA transfer essentially copies a block ofmemory from one compute node to another, or between RAM segments ofapplications on the same compute node, from an origin to a target for aPUT operation, from a target to an origin for a GET operation.

For further explanation, FIG. 3A illustrates an example of a Point ToPoint Adapter (180) useful in parallel computers that select algorithmsfor data communications for a collective operation in a PAMI accordingto embodiments of the present invention. Point To Point Adapter (180) isdesigned for use in a data communications network optimized for point topoint operations, a network that organizes compute nodes in athree-dimensional torus or mesh. Point To Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). Point To Point Adapter (180) also provides datacommunication along a y-axis through four unidirectional datacommunications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). Point ToPoint Adapter (180) in also provides data communication along a z-axisthrough four unidirectional data communications links, to and from thenext node in the −z direction (186) and to and from the next node in the+z direction (185). For ease of explanation, the Point To Point Adapter(180) of FIG. 3A as illustrated is configured for data communications inonly three dimensions, x, y, and z, but readers will recognize thatPoint To Point Adapters optimized for point-to-point operations in aparallel computer that selects algorithms for data communicationsaccording to embodiments of the present invention may in fact beimplemented so as to support communications in two dimensions, fourdimensions, five dimensions, and so on. Several supercomputers now usefive dimensional mesh or torus networks, including, for example, IBM'sBlue Gene Q™.

For further explanation, FIG. 3B illustrates an example of a CollectiveOperations Adapter (188) useful in a parallel computer that selectsalgorithms for data communications for a collective operation in a PAMIaccording to embodiments of the present invention. Collective OperationsAdapter (188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. Collective Operations Adapter (188) in theexample of FIG. 3B provides data communication to and from two childrennodes through four unidirectional data communications links (190).Collective Operations Adapter (188) also provides data communication toand from a parent node through two unidirectional data communicationslinks (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in parallel computers that selectalgorithms for data communications for a collective operation in a PAMIaccording to embodiments of the present invention. In the example ofFIG. 4, dots represent compute nodes (102) of a parallel computer, andthe dotted lines between the dots represent data communications links(103) between compute nodes. The data communications links areimplemented with point-to-point data communications adapters similar tothe one illustrated for example in FIG. 3A, with data communicationslinks on three axis, x, y, and z, and to and fro in six directions +x(181), −x (182), +y (183), −y (184), +z (185), and −z (186). The linksand compute nodes are organized by this data communications networkoptimized for point-to-point operations into a three dimensional mesh(105). The mesh (105) has wrap-around links on each axis that connectthe outermost compute nodes in the mesh (105) on opposite sides of themesh (105). These wrap-around links form a torus (107). Each computenode in the torus has a location in the torus that is uniquely specifiedby a set of x, y, z coordinates. Readers will note that the wrap-aroundlinks in the y and z directions have been omitted for clarity, but areconfigured in a similar manner to the wrap-around link illustrated inthe x direction. For clarity of explanation, the data communicationsnetwork of FIG. 4 is illustrated with only 27 compute nodes, but readerswill recognize that a data communications network optimized forpoint-to-point operations in a parallel computer that selects algorithmsfor data communications according to embodiments of the presentinvention may contain only a few compute nodes or may contain thousandsof compute nodes. For ease of explanation, the data communicationsnetwork of FIG. 4 is illustrated with only three dimensions: x, y, andz, but readers will recognize that a data communications networkoptimized for point-to-point operations may in fact be implemented intwo dimensions, four dimensions, five dimensions, and so on. Asmentioned, several supercomputers now use five dimensional mesh or torusnetworks, including IBM's Blue Gene Q™.

For further explanation, FIG. 5 illustrates an example datacommunications network (106) optimized for collective operations byorganizing compute nodes in a tree. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with collective operations data communicationsadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in a binary tree may be characterized as a rootnode (202), branch nodes (204), and leaf nodes (206). The root node(202) has two children but no parent. The leaf nodes (206) each has aparent, but leaf nodes have no children. The branch nodes (204) each hasboth a parent and two children. The links and compute nodes are therebyorganized by this data communications network optimized for collectiveoperations into a binary tree (106). For clarity of explanation, thedata communications network of FIG. 5 is illustrated with only 31compute nodes, but readers will recognize that a data communicationsnetwork optimized for collective operations for use in parallelcomputers that select algorithms for data communications for acollective operation in a PAMI according to embodiments of the presentinvention may contain only a few compute nodes or hundreds or thousandsof compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifiesan instance of a parallel application that is executing on a computenode. That is, the rank is an application-level identifier. Using therank to identify a node assumes that only one such instance of anapplication is executing on each node. A compute node can, however,support multiple processors, each of which can support multipleprocessing cores—so that more than one process or instance of anapplication can easily be present under execution on any given computenode—or in all the compute nodes, for that matter. To the extent thatmore than one instance of an application executes on a single computenode, the rank identifies the instance of the application as such ratherthan the compute node. A rank uniquely identifies an application'slocation in the tree network for use in both point-to-point andcollective operations in the tree network. The ranks in this example areassigned as integers beginning with ‘0’ assigned to the root instance orroot node (202), ‘1’ assigned to the first node in the second layer ofthe tree, ‘2’ assigned to the second node in the second layer of thetree, ‘3’ assigned to the first node in the third layer of the tree, ‘4’assigned to the second node in the third layer of the tree, and so on.For ease of illustration, only the ranks of the first three layers ofthe tree are shown here, but all compute nodes, or rather allapplication instances, in the tree network are assigned a unique rank.Such rank values can also be assigned as identifiers of applicationinstances as organized in a mesh or torus network.

For further explanation, FIG. 6 sets forth a block diagram of an exampleprotocol stack useful in parallel computers that select algorithms fordata communications for a collective operation in a PAMI according toembodiments of the present invention. The example protocol stack of FIG.6 includes a hardware layer (214), a system messaging layer (212), anapplication messaging layer (210), and an application layer (208). Forease of explanation, the protocol layers in the example stack of FIG. 6are shown connecting an origin compute node (222) and a target computenode (224), although it is worthwhile to point out that in embodimentsthat effect DMA data transfers, the origin compute node and the targetcompute node can be the same compute node. The granularity of connectionthrough the system messaging layer (212), which is implemented with aPAMI (218), is finer than merely compute node to compute node—because,again, communications among endpoints often is communications amongendpoints on the same compute node. For further explanation, recall thatthe PAMI (218) connects endpoints, connections specified by combinationsof clients, contexts, and tasks, each such combination being specific toa thread of execution on a compute node, with each compute node capableof supporting many threads and therefore many endpoints. Every endpointtypically can function as both an origin endpoint or a target endpointfor data transfers through a PAMI, and both the origin endpoint and itstarget endpoint can be located on the same compute node. So an origincompute node (222) and its target compute node (224) can in fact, andoften will, be the same compute node.

The application layer (208) provides communications among instances of aparallel application (158) running on the compute nodes (222, 224) byinvoking functions in an application messaging module (216) installed oneach compute node. Communications among instances of the applicationthrough messages passed between the instances of the application.Applications may communicate messages invoking function of anapplication programming interface (‘API’) exposed by the applicationmessaging module (216). In this approach, the application messagingmodule (216) exposes a traditional interface, such as an API of an MPIlibrary, to the application program (158) so that the applicationprogram can gain the benefits of a PAMI, reduced network traffic,callback functions, and so on, with no need to recode the application.Alternatively, if the parallel application is programmed to use PAMIfunctions, the application can call the PAMI functions directly, withoutgoing through the application messaging module.

The example protocol stack of FIG. 6 includes a system messaging layer(212) implemented here as a PAMI (218). The PAMI provides system-leveldata communications functions that support messaging in the applicationlayer (602) and the application messaging layer (610). Such system-levelfunctions are typically invoked through an API exposed to theapplication messaging modules (216) in the application messaging layer(210). Although developers can in fact access a PAMI API directly bycoding an application to do so, a PAMI's system-level functions in thesystem messaging layer (212) in many embodiments are isolated from theapplication layer (208) by the application messaging layer (210), makingthe application layer somewhat independent of system specific details.With an application messaging module presenting a standard MPI API to anapplication, for example, with the application messaging module retooledto use the PAMI to carry out the low-level messaging functions, theapplication gains the benefits of a PAMI with no need to incur theexpense of reprogramming the application to call the PAMI directly.Because, however, some applications will in fact be reprogrammed to callthe PAMI directly, all entities in the protocol stack above the PAMI areviewed by PAMI as applications. When PAMI functions are invoked byentities above the PAMI in the stack, the PAMI makes no distinctionwhether the caller is in the application layer or the applicationmessaging layer, no distinction whether the caller is an application assuch or an MPI library function invoked by an application. As far as thePAMI is concerned, any caller of a PAMI function is an application.

The protocol stack of FIG. 6 includes a hardware layer (634) thatdefines the physical implementation and the electrical implementation ofaspects of the hardware on the compute nodes such as the bus, networkcabling, connector types, physical data rates, data transmissionencoding and many other factors for communications between the computenodes (222) on the physical network medium. In parallel computers thatselect algorithms for data communications with DMA controllers accordingto embodiments of the present invention, the hardware layer includes DMAcontrollers and network links, including routers, packet switches, andthe like.

For further explanation, FIG. 7 sets forth a functional block diagram ofan example PAMI (218) for use in parallel computers that selectalgorithms for data communications for a collective operation in a PAMIaccording to embodiments of the present invention. The PAMI (218)provides an active messaging layer that supports both point to pointcommunications in a mesh or torus as well as collective operations,gathers, reductions, barriers, and the like in tree networks, forexample. The PAMI is a multithreaded parallel communications enginedesigned to provide low level message passing functions, many of whichare one-sided, and abstract such functions for higher level messagingmiddleware, referred to in this specification as ‘application messagingmodules’ in an application messaging layer. In the example of FIG. 7,the application messaging layer is represented by a generic MPI module(258), appropriate for ease of explanation because some form of MPI is ade facto standard for such messaging middleware. Compute nodes andcommunications endpoints of a parallel computer (102 on FIG. 1) arecoupled for data communications through such a PAMI and through datacommunications resources (294, 296, 314) that include DMA controllers,network adapters, and data communications networks through whichcontrollers and adapters deliver data communications. The PAMI (218)provides data communications among data communications endpoints, whereeach endpoint is specified by data communications parameters for athread of execution on a compute node, including specifications of aclient, a context, and a task.

The PAMI (218) in this example includes PAMI clients (302, 304), tasks(286, 298), contexts (190, 292, 310, 312), and endpoints (288, 300). APAMI client is a collection of data communications resources (294, 295,314) dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. Data communications resources assigned incollections to PAMI clients are explained in more detail below withreference to FIGS. 8A and 8B. PAMI clients (203, 304 on FIG. 7) enablehigher level middleware, application messaging modules, MPI libraries,and the like, to be developed independently so that each can be usedconcurrently by an application. Although the application messaging layerin FIG. 7 is represented for example by a single generic MPI module(258), in fact, a PAMI, operating multiple clients, can support multiplemessage passing libraries or application messaging modulessimultaneously, a fact that is explained in more detail with referenceto FIG. 9. FIG. 9 sets forth a functional block diagram of an examplePAMI (218) useful in parallel computers that select algorithms for datacommunications for a collective operation in a PAMI according toembodiments of the present invention in which the example PAMI operates,on behalf of an application (158), with multiple application messagingmodules (502-510) simultaneously. The application (158) can havemultiple messages in transit simultaneously through each of theapplication messaging modules (502-510). Each context (512-520) carriesout, through post and advance functions, data communications for theapplication on data communications resources in the exclusivepossession, in each client, of that context. Each context carries outdata communications operations independently and in parallel with othercontexts in the same or other clients. In the example FIG. 9, eachclient (532-540) includes a collection of data communications resources(522-530) dedicated to the exclusive use of an application-level dataprocessing entity, one of the application messaging modules (502-510):

-   -   IBM MPI Library (502) operates through context (512) data        communications resources (522) dedicated to the use of PAMI        client (532),    -   MPICH Library (504) operates through context (514) data        communications resources (524) dedicated to the use of PAMI        client (534),    -   Unified Parallel C (‘UPC’) Library (506) operates through        context (516) data communications resources (526) dedicated to        the use of PAMI client (536),    -   Partitioned Global Access Space (‘PGAS’) Runtime Library (508)        operates through context (518) data communications resources        (528) dedicated to the use of PAMI client (538), and    -   Aggregate Remote Memory Copy Interface (‘ARMCI’) Library (510)        operates through context (520) data communications resources        (530) dedicated to the use of PAMI client (540).

Again referring to the example of FIG. 7: The PAMI (218) includes tasks,listed in task lists (286, 298) and identified (250) to the application(158). A ‘task’ as the term is used in PAMI operations is aplatform-defined integer datatype that identifies a canonicalapplication process, an instance of a parallel application (158). Verycarefully in this specification, the term ‘task’ is always used to referonly to this PAMI structure, not the traditional use of the computerterm ‘task’ to refer to a process or thread of execution. In thisspecification, the term ‘process’ refers to a canonical data processingprocess, a container for threads in a multithreading environment. Inparticular in the example of FIG. 7, the application (158) isimplemented as a canonical process with multiple threads (251-254)assigned various duties by a leading thread (251) which itself executesan instance of a parallel application program. Each instance of aparallel application is assigned a task; each task so assigned can be aninteger value, for example, in a C environment, or a separate taskobject in a C++ or Java environment. The tasks are components ofcommunications endpoints, but are not themselves communicationsendpoints; tasks are not addressed directly for data communications inPAMI. This gives a finer grained control than was available in priormessage passing art. Each client has its own list (286, 298) of tasksfor which its contexts provide services; this allows each process topotentially reside simultaneously in two or more differentcommunications domains as will be the case in certain advanced computersusing, for example, one type of processor and network in one domain anda completely different processor type and network in another domain, allin the same computer.

The PAMI (218) includes contexts (290, 292, 310, 312). A ‘context’ asthe term is used in PAMI operations is composed of a subset of aclient's collection of data processing resources, context functions, anda work queue of data transfer instructions to be performed by use of thesubset through the context functions operated by an assigned thread ofexecution. That is, a context represents a partition of the local datacommunications resources assigned to a PAMI client. Every context withina client has equivalent functionality and semantics. Context functionsimplement contexts as threading points that applications use to optimizeconcurrent communications. Communications initiated by a local process,an instance of a parallel application, uses a context object to identifythe specific threading point that will be used to issue a particularcommunication independent of communications occurring in other contexts.In the example of FIG. 7, where the application (158) and theapplication messaging module (258) are both implemented as canonicalprocesses with multiple threads of execution, each has assigned ormapped particular threads (253, 254, 262, 264) to advance (268, 270,276, 278) work on the contexts (290, 292, 310, 312), including executionof local callbacks (272, 280). In particular, the local event callbackfunctions (272, 280) associated with any particular communication areinvoked by the thread advancing the context that was used to initiatethe communication operation in the first place. Like PAMI tasks,contexts are not used to directly address a communication destination ortarget, as they are a local resource.

Context functions, explained here with regard to references (472-482) onFIG. 9, include functions to create (472) and destroy (474) contexts,functions to lock (476) and unlock (478) access to a context, andfunctions to post (480) and advance (480) work in a context. For ease ofexplanation, the context functions (472-482) are illustrated in only oneexpanded context (512); readers will understand, however, that all PAMIcontexts have similar context functions. The create (472) and destroy(474) functions are, in an object-oriented sense, constructors anddestructors. In the example embodiments described in thisspecifications, post (480) and advance (482) functions on a context arecritical sections, not thread safe. Applications using suchnon-reentrant functions must somehow ensure that critical sections areprotected from re-entrant use. Applications can use mutual exclusionlocks to protect critical sections. The lock (476) and unlock (478)functions in the example of FIG. 9 provide and operate such a mutualexclusion lock to protect the critical sections in the post (480) andadvance (482) functions. If only a single thread posts or advances workon a context, then that thread need never lock that context. To theextent that progress is driven independently on a context by a singlethread of execution, then no mutual exclusion locking of the contextitself is required—provided that no other thread ever attempts to call afunction on such a context. If more than one thread will post or advancework on a context, each such thread must secure a lock before calling apost or an advance function on that context. This is one reason why itis probably a preferred architecture, given sufficient resources, toassign one thread to operate each context. Progress can be driven withadvance (482) functions concurrently among multiple contexts by usingmultiple threads, as desired by an application—shown in the example ofFIG. 7 by threads (253, 254, 262, 264) which advance work concurrently,independently and in parallel, on contexts (290, 292, 310, 312).

Posts and advances (480, 482 on FIG. 9) are functions called on acontext, either in a C-type function with a context ID as a parameter,or in object oriented practice where the calling entity possesses areference to a context or a context object as such and the posts andadvances are member methods of a context object. Again referring to FIG.7: Application-level entities, application programs (158) andapplication messaging modules (258), post (266, 274) data communicationsinstructions, including SENDs, RECEIVEs, PUTs, GETs, and so on, to thework queues (282, 284, 306, 308) in contexts and then call advancefunctions (268, 270, 276, 278) on the contexts to progress specific dataprocessing and data communications that carry out the instructions. Thedata processing and data communications effected by the advancefunctions include specific messages, request to send (‘RTS’) messages,acknowledgments, callback execution, transfers of transfer data orpayload data, and so on. Advance functions therefore operate generallyby checking a work queue for any new instructions that need to beinitiated and checking data communications resources for any incomingmessage traffic that needs to be administered as well as increases instorage space available for outgoing message traffic, with callbacks andthe like. Advance functions also carry out or trigger transfers oftransfer data or payload data.

In at least some embodiments, a context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context.In the example of FIG. 7, context (290) has a subset (294) of a client's(302) data processing resources dedicated to the exclusive use of thecontext (290), and context (292) has a subset (296) of a client's (302)data processing resources dedicated to the exclusive use of the context(292). Advance functions (268, 270) called on contexts (290, 292)therefore never need to secure a lock on a data communications resourcebefore progressing work on a context—because each context (290, 292) hasexclusive use of dedicated data communications resources. Usage of datacommunications resources in this example PAMI (218), however, is notthread-safe. When data communications resources are shared amongcontexts, mutual exclusion locks are needed. In contrast to theexclusive usage of resources by contexts (290, 292), contexts (310, 312)share access to their client's data communications resource (314) andtherefore do not have data communications resources dedicated toexclusive use of a single context. Contexts (310, 312) therefore alwaysmust secure a mutual exclusion lock on a data communications resourcebefore using the resource to send or receive administrative messages ortransfer data.

For further explanation, here is an example pseudocode Hello Worldprogram for an application using a PAMI:

int main(int argc, char ** argv) { PAMI_client_t client; PAMI_context_tcontext; PAMI_result_t status = PAMI_ERROR; const char *name = “PAMI”;status = PAMI_Client_initialize(name, &client); size_t_n = 1; status =PAMI_Context_createv(client, NULL, 0, &context, _n);PAMI_configuration_t configuration; configuration.name = PAMI_TASK_ID;status = PAMI_Configuration_query(client, &configuration); size_ttask_id = configuration.value.intval; configuration.name =PAMI_NUM_TASKS; status = PAMI_Configuration_query(client,&configuration); size_t num_tasks = configuration.value.intval; fprintf(stderr, “Hello process %d of %d\n”, task_id, num_tasks); status =PAMI_Context_destroy(context); status = PAMI_Client_finalize(client);return 0; }

This short program is termed ‘pseudocode’ because it is an explanationin the form of computer code, not a working model, not an actual programfor execution. In this pseudocode example, an application initializes aclient and a context for an application named “PAMI.”PAMI_Client_initialize and PAMI_Context_createv are initializationfunctions (316) exposed to applications as part of a PAMI's API. Thesefunctions, in dependence upon the application name “PAMI,” pull from aPAMI configuration (318) the information needed to establish a clientand a context for the application. The application uses this segment:

-   -   PAMI_configuration_t configuration;    -   configuration.name=PAMI_TASK_ID;    -   status=PAMI_Configuration_query(client, &configuration);    -   size_t task_id=configuration.value.intval;        to retrieve its task ID and this segment:    -   configuration.name=PAMI_NUM_TASKS;    -   status=PAMI_Configuration_query(client, &configuration);    -   size_t num_tasks=configuration.value.intval;        to retrieve the number of tasks presently configured to carry        out parallel communications in the PAMI. The applications prints        “Hello process task_id of num_tasks,” where task_id is the task        ID of the subject instance of a parallel application, and        num_tasks is the number of instances of the application        executing in parallel on compute nodes. Finally, the application        destroys the context and terminates the client.

For further explanation of data communications resources assigned incollections to PAMI clients, FIG. 8A sets forth a block diagram ofexample data communications resources (220) useful in parallel computersthat select algorithms for data communications for a collectiveoperation in a PAMI according to embodiments of the present invention.The data communications resources of FIG. 8A include a gigabit Ethernetadapter (238), an Infiniband adapter (240), a Fibre Channel adapter(242), a PCI Express adapter (246), a collective operations networkconfigured as a tree (106), shared memory (227), DMA controllers (225,226), and a network (108) configured as a point-to-point torus or meshlike the network described above with reference to FIG. 4. A PAMI isconfigured with clients, each of which is in turn configured withcertain collections of such data communications resources—so that, forexample, the PAMI client (302) in the PAMI (218) in the example of FIG.7 can have dedicated to its use a collection of data communicationsresources composed of six segments (227) of shared memory, six GigabitEthernet adapters (238), and six Infiniband adapters (240). And the PAMIclient (304) can have dedicated to its use six Fibre Channel adapters(242), a DMA controller (225), a torus network (108), and five segments(227) of shared memory. And so on.

The DMA controllers (225, 226) in the example of FIG. 8A each isconfigured with DMA control logic in the form of a DMA engine (228,229), an injection FIFO buffer (230), and a receive FIFO buffer (232).The DMA engines (228, 229) can be implemented as hardware components,logic networks of a DMA controller, in firmware, as software operatingan embedded controller, as various combinations of software, firmware,or hardware, and so on. Each DMA engine (228, 229) operates on behalf ofendpoints to send and receive DMA transfer data through the network(108). The DMA engines (228, 229) operate the injection buffers (230,232) by processing first-in-first-out descriptors (234, 236) in thebuffers, hence the designation ‘injection FIFO’ and ‘receive FIFO.’

For further explanation, here is an example use case, a description ofthe overall operation of an example PUT DMA transfer using the DMAcontrollers (225, 226) and network (108) in the example of FIG. 8A: Anoriginating application (158), which is typically one instance of aparallel application running on a compute node, places a quantity oftransfer data (494) at a location in its RAM (155). The application(158) then calls a post function (480) on a context (512) of an originendpoint (352), posting a PUT instruction (390) into a work queue (282)of the context (512); the PUT instruction (390) specifies a targetendpoint (354) to which the transfer data is to be sent as well assource and destination memory locations. The application then calls anadvance function (482) on the context (512). The advance function (482)finds the new PUT instruction in its work queue (282) and inserts a datadescriptor (234) into the injection FIFO of the origin DMA controller(225); the data descriptor includes the source and destination memorylocations and the specification of the target endpoint. The origin DMAengine (225) then transfers the data descriptor (234) as well as thetransfer data (494) through the network (108) to the DMA controller(226) on the target side of the transaction. The target DMA engine(229), upon receiving the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application atthe location specified in the data descriptor and inserts into thetarget DMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) or application instancecalls an advance function (483) on a context (513) of the targetendpoint (354). The advance function (483) checks the communicationsresources assigned to its context (513) for incoming messages, includingchecking the receive FIFO (232) of the target DMA controller (226) fordata descriptors that specify the target endpoint (354). The advancefunction (483) finds the data descriptor for the PUT transfer andadvises the target application (159) that its transfer data has arrived.A GET-type DMA transfer works in a similar manner, with some differencesdescribed in more detail below, including, of course, the fact thattransfer data flows in the opposite direction. Similarly, typical SENDtransfers also operate similarly, some with rendezvous protocols, somewith eager protocols, with data transmitted in packets over the anetwork through non-DMA network adapters rather than DMA controllers.

The example of FIG. 8A includes two DMA controllers (225, 226). DMAtransfers between endpoints on separate compute nodes use two DMAcontrollers, one on each compute node. Compute nodes can be implementedwith multiple DMA controllers so that many or even all DMA transferseven among endpoints on a same compute node can be carried out using twoDMA engines. In some embodiments at least, however, a compute node, likethe example compute node (152) of FIG. 2, has only one DMA engine, sothat that DMA engine can be use to conduct both sides of transfersbetween endpoints on that compute node. For further explanation of thisfact, FIG. 8B sets forth a functional block diagram of an example DMAcontroller (225) operatively coupled to a network (108)—in anarchitecture where this DMA controller (225) is the only DMA controlleron a compute node—and an origin endpoint (352) and its target endpoint(354) are both located on the same compute node (152). In the example ofFIG. 8B, a single DMA engine (228) operates with two threads ofexecution (502, 504) on behalf of endpoints (352, 354) on a same computenode to send and receive DMA transfer data through a segment (227) ofshared memory. A transmit thread (502) injects transfer data into thenetwork (108) as specified in data descriptors (234) in an injectionFIFO buffer (230), and a receive thread (502) receives transfer datafrom the network (108) as specified in data descriptors (236) in areceive FIFO buffer (232).

The overall operation of an example PUT DMA transfer with the DMAcontrollers (225) and the network (108) in the example of FIG. 8B is: Anoriginating application (158), that is actually one of multipleinstances (158, 159) of a parallel application running on a compute node(152) in separate threads of execution, places a quantity of transferdata (494) at a location in its RAM (155). The application (158) thencalls a post function (480) on a context (512) of an origin endpoint(352), posting a PUT instruction (390) into a work queue (282) of thecontext (512); the PUT instruction specifies a target endpoint (354) towhich the transfer data is to be sent as well as source and destinationmemory locations. The application (158) then calls an advance function(482) on the context (512). The advance function (482) finds the new PUTinstruction (390) in its work queue (282) and inserts a data descriptor(234) into the injection FIFO of the DMA controller (225); the datadescriptor includes the source and destination memory locations and thespecification of the target endpoint. The DMA engine (225) thentransfers by its transmit and receive threads (502, 504) through thenetwork (108) the data descriptor (234) as well as the transfer data(494). The DMA engine (228), upon receiving by its receive thread (504)the data descriptor and the transfer data, places the transfer data(494) into the RAM (156) of the target application and inserts into theDMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) calls an advancefunction (483) on a context (513) of the target endpoint (354). Theadvance function (483) checks the communications resources assigned toits context for incoming messages, including checking the receive FIFO(232) of the DMA controller (225) for data descriptors that specify thetarget endpoint (354). The advance function (483) finds the datadescriptor for the PUT transfer and advises the target application (159)that its transfer data has arrived. Again, a GET-type DMA transfer worksin a similar manner, with some differences described in more detailbelow, including, of course, the fact that transfer data flows in theopposite direction. And typical SEND transfers also operate similarly,some with rendezvous protocols, some with eager protocols, with datatransmitted in packets over the a network through non-DMA networkadapters rather than DMA controllers.

By use of an architecture like that illustrated and described withreference to FIG. 8B, a parallel application or an application messagingmodule that is already programmed to use DMA transfers can gain thebenefit of the speed of DMA data transfers among endpoints on the samecompute node with no need to reprogram the applications or theapplication messaging modules to use the network in other modes. In thisway, an application or an application messaging module, alreadyprogrammed for DMA, can use the same DMA calls through a same API forDMA regardless whether subject endpoints are on the same compute node oron separate compute nodes.

For further explanation, FIG. 10 sets forth a functional block diagramof example endpoints useful in parallel computers that select algorithmsfor data communications for a collective operation in a PAMI accordingto embodiments of the present invention. In the example of FIG. 10, aPAMI (218) is implemented with instances on two separate compute nodes(152, 153) that include four endpoints (338, 340, 342, 344). Theseendpoints are opaque objects used to address an origin or destination ina process and are constructed from a (client, task, context) tuple.Non-DMA SEND and RECEIVE instructions as well as DMA instructions suchas PUT and GET address a destination by use of an endpoint object orendpoint identifier.

Each endpoint (338, 340, 342, 344) in the example of FIG. 10 is composedof a client (302, 303, 304, 305), a task (332, 333, 334, 335), and acontext (290, 292, 310, 312). Using a client a component in thespecification of an endpoint disambiguates the task and contextidentifiers, as these identifiers may be the same for multiple clients.A task is used as a component in the specification of an endpoint toconstruct an endpoint to address a process accessible through a context.A context in the specification of an endpoint identifies, refers to, orrepresents the specific context associated with a destination or targettask—because the context identifies a specific threading point on atask. A context offset identifies which threading point is to process aparticular communications operation. Endpoints enable “crosstalk” whichis the act of issuing communication on a local context with a particularcontext offset that is directed to a destination endpoint with nocorrespondence to a source context or source context offset.

For efficient utilization of storage in an environment where multipletasks of a client reside on the same physical compute node, anapplication may choose to write an endpoint table (288, 300 on FIG. 7)in a segment of shared memory (227, 346, 348). It is the responsibilityof the application to allocate such segments of shared memory andcoordinate the initialization and access of any data structures sharedbetween processes. This includes any endpoint objects which are createdby one process or instance of an application and read by anotherprocess.

Endpoints (342, 344) on compute node (153) serve respectively twoapplication instances (157, 159). The tasks (334, 336) in endpoints(342, 344) are different. The task (334) in endpoint (342) is identifiedby the task ID (249) of application (157), and the task (336) inendpoint (344) is identified by the task ID (251) of application (159).The clients (304, 305) in endpoints (342, 344) are different, separateclients. Client (304) in endpoint (342) associates data communicationsresources (e.g., 294, 296, 314 on FIG. 7) dedicated exclusively to theuse of application (157), while client (305) in endpoint (344)associates data communications resources dedicated exclusively to theuse of application (159). Contexts (310, 312) in endpoints (342, 344)are different, separate contexts. Context (310) in endpoint (342)operates on behalf of application (157) a subset of the datacommunications resources of client (304), and context (312) in endpoint(344) operates on behalf of application (159) a subset of the datacommunications resources of client (305).

Contrasted with the PAMIs (218) on compute node (153), the PAMI (218) oncompute node (152) serves only one instance of a parallel application(158) with two endpoints (338, 340). The tasks (332, 333) in endpoints(338, 340) are the same, because they both represent a same instance ofa same application (158); both tasks (332,333) therefore are identified,either with a same variable value, references to a same object, or thelike, by the task ID (250) of application (158). The clients (302, 303)in endpoints (338, 340) are optionally either different, separateclients or the same client. If they are different, each associates aseparate collection of data communications resources. If they are thesame, then each client (302, 303) in the PAMI (218) on compute node(152) associates a same set of data communications resources and isidentified with a same value, object reference, or the like. Contexts(290, 292) in endpoints (338, 340) are different, separate contexts.Context (290) in endpoint (338) operates on behalf of application (158)a subset of the data communications resources of client (302) regardlesswhether clients (302, 303) are the same client or different clients, andcontext (292) in endpoint (340) operates on behalf of application (158)a subset of the data communications resources of client (303) regardlesswhether clients (302, 303) are the same client or different clients.Thus the tasks (332, 333) are the same; the clients (302, 303) can bethe same; and the endpoints (338, 340) are distinguished at least bydifferent contexts (290, 292), each of which operates on behalf of oneof the threads (251-254) of application (158), identified typically by acontext offset or a threading point.

Endpoints (338, 340) being as they are on the same compute node (152)can effect DMA data transfers between endpoints (338, 340) through DMAcontroller (225) and a segment of shared local memory (227). In theabsence of such shared memory (227), endpoints (338, 340) can effect DMAdata transfers through the DMA controller (225) and the network (108),even though both endpoints (338, 340) are on the same compute node(152). DMA transfers between endpoint (340) on compute node (152) andendpoint (344) on another compute node (153) go through DMA controllers(225, 226) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (338) on compute node (152)and endpoint (342) on another compute node (153) also go through DMAcontrollers (225, 226) and either a network (108) or a segment of sharedremote memory (346). The segment of shared remote memory (346) is acomponent of a Non-Uniform Memory Access (‘NUMA’) architecture, asegment in a memory module installed anywhere in the architecture of aparallel computer except on a local compute node. The segment of sharedremote memory (346) is ‘remote’ in the sense that it is not installed ona local compute node. A local compute node is ‘local’ to the endpointslocated on that particular compute node. The segment of shared remotememory (346), therefore, is ‘remote’ with respect to endpoints (338,340) on compute node (158) if it is in a memory module on compute node(153) or anywhere else in the same parallel computer except on computenode (158).

Endpoints (342, 344) being as they are on the same compute node (153)can effect DMA data transfers between endpoints (342, 344) through DMAcontroller (226) and a segment of shared local memory (348). In theabsence of such shared memory (348), endpoints (342, 344) can effect DMAdata transfers through the DMA controller (226) and the network (108),even though both endpoints (342, 344) are on the same compute node(153). DMA transfers between endpoint (344) on compute node (153) andendpoint (340) on another compute node (152) go through DMA controllers(226, 225) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (342) on compute node (153)and endpoint (338) on another compute node (158) go through DMAcontrollers (226, 225) and either a network (108) or a segment of sharedremote memory (346). Again, the segment of shared remote memory (346) is‘remote’ with respect to endpoints (342, 344) on compute node (153) ifit is in a memory module on compute node (158) or anywhere else in thesame parallel computer except on compute node (153).

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of algorithm selection for data communications for acollective operation in a PAMI of a parallel computer according toembodiments of the present invention. The method of FIG. 11 isimplemented in a PAMI (218) of a parallel computer composed of a numberof compute nodes that execute a parallel application, like thosedescribed above in this specification with reference to FIGS. 1-10. ThePAMI (218) includes data communications endpoints (352, 354), with eachendpoint specifying data communications parameters for a thread (251,252) of execution on a compute node (102 on FIG. 1), includingspecifications of a client (302, 303, 304, 305 on FIG. 10), a context(290, 292, 310, 312 on FIG. 10), and a task (332, 333, 334, 336 on FIG.10), all as described above in this specification with reference toFIGS. 1-10. The compute nodes (102 on FIG. 1) and endpoints on thecompute nodes are coupled for data communications through the PAMI (218)and through data communications resources (e.g., 238, 240, 242, 246,106, 227, 225, 108 on FIG. 8A).

The method of FIG. 11 includes associating (360) in the PAMI datacommunications algorithms (380) and bit masks (378) so that eachalgorithm is associated with a separate bit mask. This association isimplemented, for example, in a table (377) in which each recordassociates a bit mask (378) and a data communications algorithm (380).The implementation as a table is not a requirement of the invention;other structures can associate bit masks with algorithms, e.g., linkedlists, arrays, arrays of C-style structures, and so on. The bits masksand their associated or corresponding algorithms are ‘associated’ in thesense that they are stored in association with one another, in the samerecord of a table, the fields of a same C structure, in the elements ofa same row in a two dimensional array, and so on. For this reason, sucha bit mask stored in association with an algorithm is referred togenerally in this specification as an ‘associated bit mask.’ Each bit ineach mask represents the presence or absence of a characteristic of acollective instruction to be executed by use of the algorithm associatedwith that mask, a fact that is explained further with reference to FIG.12.

FIG. 12 sets forth a diagram of an example 16-bit bit mask useful inalgorithm selection for data communications for a collective operationin a PAMI of a parallel computer according to embodiments of the presentinvention. The use of 16 bits is not a limitation of the presentinvention. Bit masks used in algorithm selection for data communicationsfor a collective operation in a PAMI of a parallel computer according toembodiments of the present invention can have any number of bits thatmay occur to those of skill in the art, 8, 16, 32, 64, 128, and so on.Each bit in the mask represents the presence or absence of acharacteristic of a collective instruction to be executed by use of thealgorithm associated with the mask. In this example the bits arenumbered from least significant to most significant, right to left,0-15, and each bit signifies:

-   -   Bit 15: That a collective operation specifies a data        communications message of a size between 1 and 4048 bytes.    -   Bit 14: That a collective operation specifies a data        communications message of a size greater than 4048 bytes. This        illustrates that a set of two or more binary bits can make a        Boolean representation of a non-Boolean quantity by having each        bit represent a separate range of values.    -   Bit 13: Threading mode for a collective operation. That is,        whether a collective operation is called in an environment where        tasks and endpoints are implemented with a single thread of        execution or with multiple threads of execution.    -   Bit 12: Whether the number of PAMI tasks in which a collective        operation is to execute is odd or even. Also pertinent to        algorithm selection, not shown here, would be a bit to represent        whether an MPI rank count were odd or even.    -   Bit 11: Whether the number of PAMI tasks among which a collect        operation is to execute is a power of two. Also pertinent to        algorithm selection, not shown here, would be a bit to represent        whether an MPI rank count were a power of two.    -   Bit 10: Whether a message buffer in use by a collective        operation is aligned on a memory boundary, for example, an        8-byte boundary or a 16-byte boundary.    -   Bit 9: Number of MPI ranks Although represented with a single        bit for ease of illustration, in an embodiment, this quantity        would use more than one bit, indicating ranges as was done above        with bits 15 and 14 for messages size.    -   Bit 8: Number of PAMI tasks. Although represented with a single        bit for ease of illustration, in an embodiment, this quantity        would use more than one bit, indicating ranges as was done above        with bits 15 and 14 for messages size.    -   Bit 7: MPI communicator shape—whether a collective operation is        to execute in a communicator that is irregular in shape or        regular such as a rectangle, square, or cube.    -   Bit 6: MPI communicator size—the number of ranks in an MPI        communicator in which a collective is to execute.    -   Bit 5: PAMI geometry shape—whether a collective operation is to        execute in a geometry of PAMI tasks that is irregular in shape        or regular such as a rectangle, square, or cube.    -   Bit 4: PAMI geometry size—the number of tasks in a PAMI geometry        in which a collective is to execute.    -   Bit 3: Tree network available—whether a collective operation has        available to it for a data communications operations a tree        network.    -   Bit 2: Torus network available—whether a collective operation        has available to it for a data communications operations a torus        network.    -   Bit 1: DMA available—whether a collective operation has        available to it for data communications Direct Memory Access        functionality.    -   Bit 0: Shared memory available—whether a collective operation        has available to it for data communications one or more segments        of shared memory.

The bit mask size of 16 bits was selected for this example to easeillustration and explanation. As a practical matter, embodiments willoften use larger bit masks. Here are some examples of use of bits torepresent characteristics of collective operations for which more bitswould be needed:

-   -   Additional ranges of message sizes,    -   Multiple ranges of MPI communicator sizes,    -   Multiple ranges of PAMI geometry sizes,    -   an indication whether a collective operation is to execute in        MPI commworld or in a sub-communicator,    -   and so on.

Of course readers will now recognize that PAMI collective operations areamenable to call directly from an application as such with noinvolvement of MPI at all, and, for such collective operations, theinformation encoded as MPI characteristics would not be relevant. Still,bit masks according to embodiments of the present application often willsupport PAMI calls from MPI libraries, and, in those circumstances,knowledge of the collective operation's MIP environment will often beused in algorithm selection.

TABLE 1 Bit Masks Associated With Data Communications Algorithms BitMask Algorithm 1011110001010110 A0 0111000110101011 A1 1011100010101101A2 1010101000111010 A3 . . . . . .

Bit masks can be associated with data communications algorithms asillustrated in Table 1, for example. Each record in Table 1 representsan association of a bit masks and a data communications algorithm, whereeach algorithm is associated with a separate bit mask. The bit masks areexpressed with 16 bits each, and the algorithms are represented as A0,A1, A2, and so on. Examples of data communications algorithms that areuseful or that can be improved to be useful for algorithm selectionaccording to embodiments of the present invention include the followingas well as others that will occur to those of skill in the art:

-   -   rendezvous network-based send algorithms in which both the        origin endpoint and the target endpoint communicate and        participate in a data transfer, good for longer messages,        typically composed of handshakes transferring header information        followed by packet switched messaging or DMA operations to        transfer payload data,    -   eager network-based send algorithms in which only the origin        endpoint conducts a data transfer, merely informing the target        endpoint that the transfer has occurred, but requiring no        communications or other participation from the target endpoint,    -   rendezvous send algorithms with operations conducted, not        through a network, but through shared memory, in which both the        origin endpoint and the target endpoint communicate and        participate in a data transfer,    -   eager send algorithms conducted, not through a network, but        through shared memory, in which only the origin endpoint        conducts a data transfer, merely informing the target endpoint        that the transfer has occurred, but requiring no communications        or other participation from the target endpoint,    -   network-based DMA PUT algorithms, useful for fast transfers of        small messages, sometimes containing header data and payload        data in a single transfer or packet—DMA algorithms also can be        used as components of other algorithm—as for example a SEND        algorithm that does an origin-target handshake and then conducts        payload transfers with PUTs,    -   DMA PUT algorithms with transfers through shared memory, again        useful for fast transfers of small messages, sometimes        containing header data and payload data in a single transfer or        packet—DMA algorithms also can be used as components of other        algorithm—as for example a SEND algorithm that does an        origin-target handshake through a segment of shared memory and        then conducts payload transfers with PUTs,    -   data communications algorithms based on DMA GET operations,        either networked or through shared memory, and    -   data communications algorithms that include eager or rendezvous        RECEIVE operations, either with send-side matching of SENDs or        with receive-side matching.

Again referring to FIG. 11: The method of FIG. 11 also includesreceiving (362) in an origin endpoint (352) of the PAMI (218) acollective instruction (358), where the instruction specifiestransmission of a data communications message (382) from the originendpoint (352) to at least one target endpoint (354). The messagingtarget is referred to as ‘at least one’ target endpoint because manycollective instruction result in more than one message. To the extentthat the algorithm includes a transmission from a root to all thebranches and leaves of a tree, a collective instruction results in manyoutgoing transmissions. To the extent that the root transmits only toits two immediate children, and they to theirs, and so on, thecollective instruction results in two messages transmitted from the rootand from all the child branch nodes below the root—and possibly zeromessages from the leaf nodes. To the extent that a collective operationis implemented on a mesh or torus, a root can inject a collective-basedmessage into a torus with as little as a single transmission of a singlemessage, depending on the shape of the torus. And so on. Examples ofcollective instructions include broadcasts, scatters, reductions, andgathers. Examples of data communications messages include networkoriented point-to-point SEND instructions, DMA GETs and PUTs, as well ascomponent messages such as requests-to-send, acknowledgment messages,and the like. The collective instruction (358) is received (362) in theorigin endpoint (352) by an application's post of the instruction to awork queue of a context of the origin endpoint (352).

The method of FIG. 11 also includes constructing by the origin endpointa bit mask for the received collective instruction. The bit mask iscomposed of a set of bits like the one illustrated in FIG. 12, forexample, where each bit in the mask represents a characteristic of thereceived collective instruction, that is, the binary/Boolean presence orabsence of a characteristic of the received collective instruction. Thepoint in data processing at which an advance function of a context ofthe origin endpoint finds the collective instruction (358) posted in itswork queue and begins processing the collective instruction byconstructing a bit mask and using the constructed bit mask to select adata communication algorithm is referred to in this specification as a‘call site,’ the collective instruction's call site. At the call site,all the information needed to construct a bit mask, all thecharacteristics of the collective operation, are known or available tothe advance function, message size, threading mode, task count,communicator size, network availability, and so on. The advance functionconstructs the bit mask by setting the bits in the mask according to thecharacteristics of the collective instruction, including its executionenvironment. Using the bit mask form illustrated in FIG. 12, forexample, the advance function would set bit 15 to 1 if the message sizeis less than 4 kilobytes, otherwise to 0. The advance function would setbit 14 to 1 if the message size is greater than 4 kilobytes, otherwiseto 0, The advance function would set bit 13 to 1 if the threading modeis multi-threading, otherwise to 0. And so on.

The method of FIG. 11 also includes selecting (364) by the originendpoint (352), from among the associated algorithms (380) in dependenceupon the constructed bit mask (381), a data communications algorithm(386) for use in executing the received collective instruction (358). Anadvance function (482), also called by the application that posted thecollective instruction, finds the collective instruction (358) in itswork queue and then finds in table (377) an associated bit mask (378)that matches the constructed bit mask (381). The advance function takesthe algorithm (380) associated with that range as the selected algorithmfor use in transmitting the message (382). In this example, selecting(364) a data communications algorithm is carried out by iterativelybitwise comparing (392) with the constructed bit mask (381) the bitmasks (378) associated with data communications algorithms (380) until amatch is found, and then taking (394) as the selected datacommunications algorithm ( ) the data communications algorithmassociated with the bit mask that matches the constructed bit mask. Thebitwise comparisons can be implemented with a bitwise logical ANDoperation, ‘&,’ explained, including the selection of the algorithm,with pseudocode as follows:

find_matching_bit_mask( constructed bit mask) { A = constructed bitmask; B = first associated bit mask from table; while( A&B is not equalto A) B = next associated bit mask from table; return(the algorithmassociated with B); }

The method of FIG. 11 also includes executing (366) the receivedcollective instruction (358) by the origin endpoint (352), includingtransmitting, according to the selected data communications algorithm(386) from the origin endpoint (352) to the target endpoint (354), thedata communications message (382).

There is no requirement for the entire bit mask to be constructed onlyat or near the call site of a collective instruction. Some of thecharacteristics of the collective instruction simply will not be knownuntil arriving at the call site, the message size, for example, so thatit is always correct to speak of the construction of the message, in itsfinal form at least, as being constructed at run time at or near thecall site. Other characteristics of a collective operation, however, canbe known at earlier points in overall data processing, so that a bitmask can be partially preconstructed at points of data processingearlier than the call site of a pertinent collective instruction, at,for example, the point in data processing where an application or anapplication messaging module initializes a PAMI or initializes an MPIcommunicator. For further explanation of bit mask preconstruction, FIG.13 sets forth a flow chart illustrating a further example method ofalgorithm selection for data communications for a collective instructionin a PAMI of a parallel computer according to embodiments of the presentinvention. Like the method of FIG. 11, the method of FIG. 11 also isimplemented in a PAMI (218) of a parallel computer composed of a numberof compute nodes that execute a parallel application, like thosedescribed above in this specification with reference to FIGS. 1-10. ThePAMI (218) includes data communications endpoints (352, 354), with eachendpoint specifying data communications parameters for a thread (251,252) of execution on a compute node (102 on FIG. 1), includingspecifications of a client (302, 303, 304, 305 on FIG. 10), a context(290, 292, 310, 312 on FIG. 10), and a task (332, 333, 334, 336 on FIG.10), all as described above in this specification with reference toFIGS. 1-10. The compute nodes (102 on FIG. 1) and endpoints on thecompute nodes are coupled for data communications through the PAMI (218)and through data communications resources (e.g., 238, 240, 242, 246,106, 227, 225, 108 on FIG. 8A).

The method of FIG. 13 is also similar to the method of FIG. 11,including as it does, associating (360) in the PAMI data communicationsalgorithms (380) and bit masks (378), receiving (362) in an originendpoint (352) of the PAMI (218) a collective instruction (358),constructing (363) a bit mask for the received collective instruction(358), selecting (364) a data communications algorithm (386), andexecuting (366) the received collective instruction (358), transmittingthe data communications message (382) according to the selectedalgorithm (386).

The method of FIG. 13, however, also includes initializing (396) thePAMI and, upon initialing the PAMI, partially preconstructing (399) abit mask (398) for each type of collective instruction; and the methodof FIG. 13 also includes initializing (397) an MPI communicator, and,upon initialing the communicator, partially preconstructing (397) a bitmask for each type of collective instruction. The second partialpreconstruction can be a follow on from the first, so that, uponinitializing the PAMI, either the initialization function of the PAMI oran application function or a function of an application messaging modulecan establish a bit mask for each type of collective instruction, onefor BROADCAST, one for SCATTER, one for REDUCE, one for GATHER, and soon, and then partially preconstruct each of these with thecharacteristics known at the time, for example, threading mode, numberof PAMI tasks, available networks, and the like. Then upon initializingthe communicator, the application or a function of an applicationmessaging module or an initialization routine of the PAMI can take thenow existing but incomplete bit masks and further preconstruct them,adding characteristics known now but not known earlier such as, forexample, communicator size, number of MPI ranks in the communicator,communicator shape, and so on. In this way, it is possible topreconstruct bit masks with all the characteristics of a collectiveoperation except the items that can only be known at or near the callsite, the exact collective type, BROADCAST, SCATTER, and so on, and, forexample, message size.

In the example method of FIG. 13, constructing (363) a bit mask for thereceived collective instruction includes constructing (300) the bit maskfor the received collective instruction from one of the partiallypreconstructed bit masks (398). If the collective type is BROADCAST, anadvance function of the origin endpoint (352) would construct theconstructed bit mask (381) from the partially preconstructed bit mask(398) for BROADCASTs. If the collective type is SCATTER, an advancefunction of the origin endpoint (352) would construct the constructedbit mask (381) from the partially preconstructed bit mask (398) forSCATTERs. In this way, the data processing burden, expense, and latencyof constructing the bit mask at the call site is greatly reduced.

Example embodiments of the present invention are described largely inthe context of a fully functional parallel computer that selectsalgorithms for data communications for a collective operation in a PAMI.Readers of skill in the art will recognize, however, that the presentinvention also may be embodied in a computer program product disposedupon computer readable storage media for use with any suitable dataprocessing system. Such computer readable storage media may be anystorage medium for machine-readable information, including magneticmedia, optical media, or other suitable media. Examples of such mediainclude magnetic disks in hard drives or diskettes, compact disks foroptical drives, magnetic tape, and others as will occur to those ofskill in the art. Persons skilled in the art will immediately recognizethat any computer system having suitable programming means will becapable of executing the steps of the method of the invention asembodied in a computer program product. Persons skilled in the art willrecognize also that, although some of the example embodiments describedin this specification are oriented to software installed and executingon computer hardware, nevertheless, alternative embodiments implementedas firmware or as hardware are well within the scope of the presentinvention.

As will be appreciated by those of skill in the art, aspects of thepresent invention may be embodied as method, apparatus or system, orcomputer program product. Accordingly, aspects of the present inventionmay take the form of an entirely hardware embodiment or an embodimentcombining software and hardware aspects (firmware, resident software,micro-code, microcontroller-embedded code, and the like) that may allgenerally be referred to herein as a “circuit,” “module,” “system,” or“apparatus.” Furthermore, aspects of the present invention may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

Any combination of one or more computer readable media may be utilized.Such a computer readable medium may be a computer readable signal mediumor a computer readable storage medium. A computer readable storagemedium may be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described in this specificationwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof computer apparatus, methods, and computer program products accordingto various embodiments of the present invention. In this regard, eachblock in a flowchart or block diagram may represent a module, segment,or portion of code, which is composed of one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that, in some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A parallel computer that selects an algorithm for data communications for a collective operation in a parallel active messaging interface (‘PAMI’) of the parallel computer, the parallel computer comprising a plurality of compute nodes that execute a parallel application, the PAMI comprising data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications through the PAMI and through data communications resources, the compute nodes comprising computer processors operatively coupled to computer memory having disposed within it computer program instructions that, when executed by the computer processors, cause the parallel computer to function by: associating in the PAMI data communications algorithms and bit masks so that each algorithm is associated with a separate bit mask, each bit in each mask representing the presence or absence of a characteristic of a collective instruction to be executed by use of the algorithm associated with that mask; initializing the PAMI; and partially preconstructing, upon initialing the PAMI, a bit mask for each type of collective instruction; receiving in an origin endpoint of the PAMI a collective instruction, the collective instruction specifying transmission of a data communications message from the origin endpoint to at least one target endpoint; constructing by the origin endpoint a bit mask for the received collective instruction, each bit in the mask representing a characteristic of the received collective instruction, wherein constructing a bit mask for the received collective instruction further comprises constructing the bit mask for the received collective instruction from one of the partially preconstructed bit masks; selecting by the origin endpoint, from the associated data communications algorithms in dependence upon the constructed bit mask, a data communications algorithm for use in executing the received collective instruction; and executing the received collective instruction by the origin endpoint, including transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
 2. The parallel computer of claim 1 wherein selecting a data communications algorithm further comprises: iteratively bitwise comparing with the constructed bit mask the bit masks associated with data communications algorithms until a match is found; and taking as the selected data communications algorithm the data communications algorithm associated with the bit mask that matches the constructed bit mask.
 3. The parallel computer of claim 1 further comprising computer program instructions that cause the parallel computer to function by: initializing a Message Passing Interface (‘MPI’) communicator; and partially preconstructing, upon initializing the MPI communicator, a bit mask for each type of collective instruction; wherein constructing a bit mask for the received collective instruction further comprises constructing the bit mask for the received collective instruction from one of the partially preconstructed bit masks.
 4. The parallel computer of claim 1 wherein: each client comprises a collection of data communications resources dedicated to the exclusive use of an application-level data processing entity; each context comprises a subset of the collection of data processing resources of a client, context functions, and a work queue of data transfer instructions to be performed by use of the subset through the context functions operated by an assigned thread of execution; and each task represents a process of execution of the parallel application.
 5. The parallel computer of claim 1 wherein each context carries out, through post and advance functions, data communications for the parallel application on data communications resources in the exclusive possession of that context.
 6. The parallel computer of claim 1 wherein each context carries out data communications operations independently and in parallel with other contexts.
 7. A computer program product for algorithm selection for data communications for a collective operation in a parallel active messaging interface (‘PAMI’) of a parallel computer, the parallel computer comprising a plurality of compute nodes that execute a parallel application, the PAMI comprising data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications through the PAMI and through data communications resources, the computer program product disposed upon a computer readable storage medium, the computer program product comprising computer program instructions that, when installed and executed, cause the parallel computer to function by: associating in the PAMI data communications algorithms and bit masks so that each algorithm is associated with a separate bit mask, each bit in each mask representing the presence or absence of a characteristic of a collective instruction to be executed by use of the algorithm associated with that mask; initializing the PAMI; and partially preconstructing, upon initialing the PAMI, a bit mask for each type of collective instruction; receiving in an origin endpoint of the PAMI a collective instruction, the collective instruction specifying transmission of a data communications message from the origin endpoint to at least one target endpoint; constructing by the origin endpoint a bit mask for the received collective instruction, each bit in the mask representing a characteristic of the received collective instruction, wherein constructing a bit mask for the received collective instruction further comprises constructing the bit mask for the received collective instruction from one of the partially preconstructed bit masks; selecting by the origin endpoint, from the associated data communications algorithms in dependence upon the constructed bit mask, a data communications algorithm for use in executing the received collective instruction; and executing the received collective instruction by the origin endpoint, including transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
 8. The computer program product of claim 7 wherein selecting a data communications algorithm further comprises: iteratively bitwise comparing with the constructed bit mask the bit masks associated with data communications algorithms until a match is found; and taking as the selected data communications algorithm the data communications algorithm associated with the bit mask that matches the constructed bit mask.
 9. The computer program product of claim 7 further comprising computer program instructions that, when installed and executed, cause the parallel computer to function by: initializing a Message Passing Interface (‘MPI’) communicator; and partially preconstructing, upon initializing the MPI communicator, a bit mask for each type of collective instruction; and wherein constructing a bit mask for the received collective instruction further comprises constructing the bit mask for the received collective instruction from one of the partially preconstructed bit masks.
 10. The computer program product of claim 7 wherein: each client comprises a collection of data communications resources dedicated to the exclusive use of an application-level data processing entity; each context comprises a subset of the collection of data processing resources of a client, context functions, and a work queue of data transfer instructions to be performed by use of the subset through the context functions operated by an assigned thread of execution; and each task represents a process of execution of the parallel application.
 11. The computer program product of claim 7 wherein each context carries out, through post and advance functions, data communications for the parallel application on data communications resources in the exclusive possession of that context. 